🔗 Who cites «Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries»

49 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · ← back to «Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries»

1. A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and BeyondarXiv:2503.21614Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.2025 · 55 citations
2. Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept SpaceConference on Neural Information Processing Systems (NeurIPS)Zhen Zhang, Xuehai He, Weixiang Yan, et al.2025 · 25 citations
3. Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Chih-Kai Yang, Neo S. Ho, Hung-yi Lee2025 · 17 citations
4. Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Zeping Yu, Yonatan Belinkov, Sophia Ananiadou2025 · 16 citations
5. A Survey on Latent ReasoningarXiv:2507.06203Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, et al.2025 · 15 citations
6. Measuring Chain of Thought Faithfulness by Unlearning Reasoning StepsConference on Empirical Methods in Natural Language Processing (EMNLP)Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovic, et al.2025 · 15 citations
7. Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language ModelsFindings of the Association for Computational LinguisticsHengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.2026 · 9 citations
8. A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across LanguagesFindings of the Association for Computational LinguisticsRaoyuan Zhao, Yihong Liu, Hinrich Schuetze, et al.2026 · 7 citations
9. Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhipeng Yang, Junzhuo Li, Siyu Xia, et al.2025 · 7 citations
10. All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other TokensConference on Empirical Methods in Natural Language Processing (EMNLP)Siddarth Mamidanna, Daking Rai, Ziyu Yao, et al.2025 · 6 citations

39 more papers of our library cite it; they have no page here yet.

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